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Parameter Calibration for a TRNSYS BIPV Model Using In Situ Test Data

Author

Listed:
  • Sang-Woo Ha

    (Department of Architectural Engineering, Inha University, Incheon 22212, Korea)

  • Seung-Hoon Park

    (Department of Architectural Engineering, Inha University, Incheon 22212, Korea)

  • Jae-Yong Eom

    (R&D Division, EAGON Windows&Doors Co., Ltd., Incheon 22107, Korea)

  • Min-Suk Oh

    (R&D Division, DAEJIN, Seoul 05839, Korea)

  • Ga-Young Cho

    (Department of Smart City Research, Seoul Institute of Technology, Seoul 03909, Korea)

  • Eui-Jong Kim

    (Department of Architectural Engineering, Inha University, Incheon 22212, Korea)

Abstract

Installing renewable energy systems for zero-energy buildings has become increasingly common; building integrated photovoltaic (BIPV) systems, which integrate PV modules into the building envelope, are being widely selected as renewable systems. In particular, owing to the rapid growth of information and communication technology, the requirement for appropriate operation and control of energy systems has become an important issue. To meet these requirements, a computational model is essential; however, some unmeasurable parameters can result in inaccurate results. This work proposes a calibration method for unknown parameters of a well-known BIPV model based on in situ test data measured over eight days; this parameter calibration was conducted via an optimization algorithm. The unknown parameters were set such that the results obtained from the BIPV simulation model are similar to the in situ measurement data. Results of the calibrated model indicated a root mean square error (RMSE) of 3.39 °C and 0.26 kW in the BIPV cell temperature and total power production, respectively, whereas the noncalibrated model, which used typical default values for unknown parameters, showed an RMSE of 6.92 °C and 0.44 kW for the same outputs. This calibration performance was quantified using measuring data from the first four days; the error increased slightly when data from the remaining four days were compared for the model tests.

Suggested Citation

  • Sang-Woo Ha & Seung-Hoon Park & Jae-Yong Eom & Min-Suk Oh & Ga-Young Cho & Eui-Jong Kim, 2020. "Parameter Calibration for a TRNSYS BIPV Model Using In Situ Test Data," Energies, MDPI, vol. 13(18), pages 1-15, September.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4935-:d:416417
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    References listed on IDEAS

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    1. Yadav, Somil & Panda, S.K., 2020. "Thermal performance of BIPV system by considering periodic nature of insolation and optimum tilt-angle of PV panel," Renewable Energy, Elsevier, vol. 150(C), pages 136-146.
    2. Alvarez-Herranz, Agustin & Balsalobre-Lorente, Daniel & Shahbaz, Muhammad & Cantos, José María, 2017. "Energy innovation and renewable energy consumption in the correction of air pollution levels," Energy Policy, Elsevier, vol. 105(C), pages 386-397.
    3. Debbarma, Mary & Sudhakar, K. & Baredar, Prashant, 2017. "Thermal modeling, exergy analysis, performance of BIPV and BIPVT: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1276-1288.
    4. Hyung Jun An & Jong Ho Yoon & Young Sub An & Eunnyeong Heo, 2018. "Heating and Cooling Performance of Office Buildings with a-Si BIPV Windows Considering Operating Conditions in Temperate Climates: The Case of Korea," Sustainability, MDPI, vol. 10(12), pages 1-19, December.
    5. Chul-sung Lee & Hyo-mun Lee & Min-joo Choi & Jong-ho Yoon, 2019. "Performance Evaluation and Prediction of BIPV Systems under Partial Shading Conditions Using Normalized Efficiency," Energies, MDPI, vol. 12(19), pages 1-16, October.
    6. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    7. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
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    Cited by:

    1. Jong Rok Lim & Woo Gyun Shin & Chung Geun Lee & Yong Gyu Lee & Young Chul Ju & Suk Whan Ko & Jung Dong Kim & Gi Hwan Kang & Hyemi Hwang, 2020. "A Study of the Electrical Output and Reliability Characteristics of the Crystalline Photovoltaic Module According to the Front Materials," Energies, MDPI, vol. 14(1), pages 1-10, December.
    2. Wijeratne, W.M. Pabasara Upalakshi & Samarasinghalage, Tharushi Imalka & Yang, Rebecca Jing & Wakefield, Ron, 2022. "Multi-objective optimisation for building integrated photovoltaics (BIPV) roof projects in early design phase," Applied Energy, Elsevier, vol. 309(C).

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